Methods and systems for applying run-to-run control and  virtual metrology to reduce equipment recovery time

ABSTRACT

Described herein are methods, apparatuses, and systems for reducing equipment repair time. In one embodiment, a computer implemented method includes collecting, with a system, data including test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility and determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data. The method further includes utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction and applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool. Applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool occurs after maintenance to reduce maintenance recovery time and to reduce requalification time.

TECHNICAL FIELD

Embodiments of the present invention relate to methods and systems forapplying run-to-run control and virtual metrology to reduce equipmentrecovery time including mean-time-to-repair (MTTR) for equipment andcomponents.

BACKGROUND

In manufacturing there are a number of processes where maintenance is arequirement either at specific intervals or in response to an event suchas a broken component or low quality production. Following themaintenance there is often a process that is executed whereby theequipment is “requalified” to a certain state such as “ready to returnto production”. This requalification can be a long and iterative processwhereby process and equipment parameters are adjusted or “tuned”. Aftera tuning iteration the equipment is evaluated, e.g., by producing a testproduct and then measuring the quality of the test product. If theevaluation indicates that the equipment or process has not met certaincriteria another tuning iteration is conducted. This iterative processis often manual, and even if partially automated, is often addressed ina univariate adhoc fashion where a few of the total set of parametersare tuned at each iteration. The time taken for these tuning iterationsis considered to be part of the mean-time-to-repair (MTTR) for theequipment.

SUMMARY

Described herein are methods, apparatuses, and systems for reducingequipment repair time. In one embodiment, a computer implemented methodincludes collecting, with a system, data including test substrate dataor other metrology data and fault detection data for maintenancerecovery of at least one manufacturing tool in a manufacturing facilityand determining, with the system, a relationship between tool parametersettings for the at least one manufacturing tool and at least somecollected data including the test substrate data. The method furtherincludes utilizing zero or more virtual metrology predictive algorithmsand at least some collected data to obtain a metrology prediction andapplying multivariate run-to-run (R2R) control modeling to obtain astate estimation including a current operating region of the at leastone manufacturing tool based on the test substrate data and obtain atleast one tool parameter adjustment for at least one target parameterfor the at least one manufacturing tool. Applying multivariaterun-to-run (R2R) control modeling to obtain tool parameter adjustmentsfor at least one manufacturing tool occurs after maintenance to reducemaintenance recovery time and to reduce requalification time.

In another embodiment, a computer system includes a memory to store oneor more sets of instructions and a processor that is coupled to thememory. The processor is configured to execute instructions to collectdata including test substrate data or metrology data and fault detectiondata for maintenance recovery of at least one manufacturing tool in amanufacturing facility, determine a relationship between tool parametersettings for the at least one manufacturing tool and at least somecollected data including the test substrate data. The method furtherincludes utilizing zero or more virtual metrology predictive algorithmsand at least some collected data to obtain a metrology prediction andapplying multivariate run-to-run (R2R) control modeling to obtain astate estimation including a current operating region of the at leastone manufacturing tool based on the test substrate data and obtain atleast one tool parameter adjustment for at least one target parameterfor the at least one manufacturing tool.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in which:

FIG. 1 is a time line of a maintenance recovery process in accordancewith one embodiment;

FIG. 2 illustrates an exemplary architecture of a manufacturingenvironment 100 for reducing maintenance time (e.g., MTTR) in accordancewith one embodiment;

FIG. 3 illustrates a flow diagram of one embodiment for a computerimplemented method of multivariate analysis utilizing run-to-run controland virtual metrology to reduce MTTR and improve G2G time during postpreventative maintenance (PM) recovery;

FIG. 4A illustrates a plot 400 of thickness versus gas flow fordifferent temperatures of a deposition tool in accordance with oneembodiment;

FIG. 4B illustrates a plot 420 of thickness versus gas flow for aspecific temperature T* of a deposition tool at post PM in accordancewith one embodiment;

FIG. 4C illustrates a plot 440 of thickness versus gas flow for atemperature T* of a deposition tool in accordance with one embodiment,and finding the recommended gas flow for a desired thickness target;

FIG. 5A illustrates lamp failure modes in accordance with oneembodiment;

FIG. 5B illustrates a diagram in which multivariate R2R control with VMis applied in accordance with one embodiment;

FIG. 6 illustrates a diagram in which multivariate R2R and VM models areapplied during maintenance recovery in accordance with one embodiment;

FIG. 7 illustrates a diagram in which multivariate R2R control with VMis applied in accordance with one embodiment;

FIG. 8 illustrates a diagram in which multivariate R2R control with VMis applied in accordance with one embodiment;

FIG. 9 illustrates an exemplary architecture of a system (e.g., anequipment engineering system (EES)), in accordance with one embodiment;and

FIG. 10 illustrates a block diagram of an exemplary computer system, inaccordance with one embodiment of the present invention.

DETAILED DESCRIPTION

Described herein are methods, apparatuses, and systems for multivariateanalysis utilizing run-to-run control and virtual metrology to reduceMTTR during post preventative maintenance (PM) recovery. In someembodiments, systems and methods for reducing the time for tuningiterations (e.g., by reducing the needed number of iterations) resultsin reduced MTTR and reduced green-to-green (G2G) time (i.e., the timebetween production-worthy states). Embodiments of this invention reducethe MTTR and G2G time by reducing the number of tuning iterationsrequired to bring an equipment or manufacturing tool to a specifiedstate after a maintenance or other non-production event. The methods andsystems of the present disclosure leverage capabilities that include“run-to-run” (R2R) control and virtual metrology (VM).

Following maintenance there is often a process that is executed in whichthe equipment is “requalified” to a certain state such as a ready toreturn to production state. FIG. 1 is a time line of a maintenancerecovery process in accordance with one embodiment. There are a numberof techniques that can be used during the production cycle (fromproducing wafers during production state 2, through predicting andscheduling maintenance state 4, seasoning state 6, requalification state8 and returning to a production state 9 after a maintenance event) thatcan improve production capabilities. MTBI is defined asmean-time-between-interrupts for a production state 2. Many of thesetechniques are components or extensions of existing Advanced ProcessControl (APC) systems capabilities and can therefore leverage the datamanagement environment provided by an existing manufacturing system'sAPC infrastructure. The specific capabilities of the solution, theirdefinitions, and example of their manner of utilization are described asfollows.

Fault Detection (FD) is the technique of monitoring and analyzingvariations in tool and/or process data to detect anomalies. Faultdetection includes both univariate and multivariate statistical analysistechniques. FD analysis is often used to identify excursions. Also FDanalysis output feed EHM, PdM and VM solutions (see below).

Equipment Health Monitoring (EHM) is the technology of monitoring toolparameters to assess the tool health as a function of deviation fromnormal behavior. EHM is not necessarily predictive in nature, but isoften a component of predictive systems. EHM can be used duringproduction (e.g., t₁₀) to monitor tool health and during the maintenancerecovery process to assess “fingerprints” indicating successfulmaintenance procedures (e.g., t₄₀), ready to move to requalification(e.g., t₅₀) or during requalification (e.g., t₆₀) to help determine if acomponent is ready to return to a production state (e.g., maintenancesuccess verification).

Predictive Maintenance (PdM) is the technology of utilizing process andequipment state information to predict when a tool or a particularcomponent in a tool might need maintenance, and then utilizing thisprediction as information to improve maintenance procedures. This couldmean predicting and avoiding unplanned downtimes and/or relaxingun-planned downtime schedules by replacing schedules with predictions.PdM solutions (e.g., PdM at t₂₂) have been illustrated to provide anumber of benefits including reduction of unscheduled downtime.

Run-to-Run (R2R) control is the technique of modifying recipe or otherequipment parameters, or the selection of control parameters betweenruns to improve processing performance. A “run” can be a batch, lot, oran individual substrate, wafer, or other product. R2R control (e.g.,t₂₀) is typically used during production to improve processes throughimproved closeness to quality targets and reduce variability of qualityparameters. R2R control (e.g., t₃₀) can also be used during amaintenance state to determine maintenance settings or processadjustments.

Virtual Metrology (VM) is the technology of prediction of post processmetrology variables (e.g., either measurable or nonmeasurable) usingprocess and wafer state information that could include upstreammetrology and/or sensor data. Typical uses of VM are to enhance the R2Rcontrol capabilities (e.g., t₁₀, t₃₀) and reduce average productioncycle time by reducing the need for metrology. Best practices and domainknowledge are procedures that leverage understanding of or experiencewith the equipment and process and related components to improvecapabilities throughout the production cycle.

In the following description, numerous details are set forth. It will beapparent, however, to one skilled in the art, that the present inventionmay be practiced without these specific details. In some instances,well-known structures and devices are shown in block diagram form,rather than in detail, in order to avoid obscuring the presentinvention.

Some portions of the detailed descriptions which follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. Unless specifically stated otherwise, asapparent from the following discussion, it is appreciated thatthroughout the description, discussions utilizing terms such as“collecting”, “predicting”, “performing”, “adjusting”, “comparing”, orthe like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

Embodiments of the present invention also relates to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Such a computer program may be stored ina computer readable storage medium, such as, but not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, each coupled to acomputer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear as set forth in thedescription below. In addition, the present invention is not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages may be used toimplement the teachings of the invention as described herein.

The present invention may be provided as a computer program product, orsoftware, that may include a machine-readable medium having storedthereon instructions, which may be used to program a computer system (orother electronic devices) to perform a process according to the presentinvention. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). Forexample, a machine (e.g., a computer) readable storage medium includesread only memory (“ROM”), random access memory (“RAM”), magnetic diskstorage media, optical storage media, flash memory devices, etc.

FIG. 2 illustrates an exemplary architecture of a manufacturingenvironment 100 for reducing maintenance time (e.g., MTTR), inaccordance with one embodiment. The manufacturing environment 100 may bea semiconductor manufacturing environment, an automotive manufacturingenvironment, aerospace equipment manufacturing environment, medicalequipment manufacturing environment, display and solar manufacturingenvironment, etc. In one embodiment, the manufacturing environment 100includes an equipment engineering system (EES) 105, a VM multi-algorithmpredictive subsystem 107 within the EES system 105 or some other systemcoupled to the EES via a network, a manufacturing execution system (MES)110, a yield management system (YMS) 120 and a consolidated data store115. The EES 105, MES 110, YMS 120 and consolidated data store 115 maybe connected via a network (not shown), such as a public network (e.g.,Internet), a private network (e.g., Ethernet or a local area Network(LAN)), or a combination thereof.

The manufacturing execution system (MES) 110 is a system that can beused to measure and control production activities in a manufacturingenvironment. The MES 110 may control some production activities (e.g.,critical production activities) or all production activities of a set ofmanufacturing equipment (e.g., all photolithography equipment in asemiconductor fabrication facility), of a manufacturing facility (e.g.,an automobile production plant), of an entire company, etc. The MES 110may include manual and computerized off-line and/or on-line transactionprocessing systems. Such systems may include manufacturing machines,metrology devices, client computing devices, server computing devices,databases, etc. that may perform functions related to processing.

In one embodiment, the MES 110 is connected with a consolidated datastore 115. The consolidated data store 115 may include databases, filesystems, or other arrangements of data on nonvolatile memory (e.g., harddisk drives, tape drives, optical drives, etc.), volatile memory (e.g.,random access memory (RAM)), or combination thereof. In one embodiment,the consolidated data store 115 includes data from multiple data stores(e.g., a YMS data store, a maintenance data store, a metrology datastore, process data stores, etc.) that are interconnected. Theconsolidated data store 115 may store, for example, historical processinformation of manufacturing recipes (e.g., temperatures, pressures,chemicals used, process times, etc.), equipment maintenance histories,inventories, etc. The consolidated data store 115 may also store datagenerated by the MES 110, YMS 120 and/or EES 105. For example, the EES105 may store fault detection and characterization data in theconsolidated data store 115, the YMS 120 may store yield analysis datain the consolidated data store 115, and the MES 110 may store historicalprocess information in the consolidated data store 115. This permitseach of the YMS 120, EES 105 and MES 110 to leverage data generated bythe other systems. The consolidated data store 115 may reside on one ormore computing devices hosting any of the MES 110, the YMS 120 and EES105, or on one or more different computing devices.

The EES 105 is a system that manages some or all operations of amanufacturing environment (e.g., factory). The EES 105 may includemanual and computerized off-line and/or on-line transaction processingsystems that may include client computing devices, server computingdevices, databases, etc. that may perform the functions of equipmenttracking, dispatching (e.g., determining what material goes to whatprocesses), product genealogy, labor tracking (e.g., personnelscheduling), inventory management, costing, electronic signaturecapture, defect and resolution monitoring, key performance indicatormonitoring and alarming, maintenance scheduling, and so on.

The EES 105 draws inferences from, reports out, and/or acts upon thecombined information that is collected and stored in the consolidateddata store 115 and/or the metrology data and process data that isreported by the MES 110. For example, EES 105 can act as an earlywarning system (e.g., predict scrap, initiate product rework, etc.),provide bottleneck analysis, provide asset management (e.g., reduceunscheduled equipment downtime, reduce scheduled equipment downtime,reduce MTTR), improve lean practices, etc. The EES 105 can be used togain an understanding of the manufacturing environment 100, and canenable a user to determine an efficiency of the manufacturingenvironment 100 and/or how to improve all or components of themanufacturing environment 100. In one embodiment, the EES 105 includescomponents (e.g., VM multi-algorithm predictive subsystem 107 having VMmodule with prediction algorithm switching module, multivariate R2Rcontroller 112, etc.) that enable the EES 105 to utilize and determinepredictive algorithms for adaptive virtual metrology, perform R2Rcontrol, reduce MTTR, and reduce green-to-green (G2G) time, which is atime period between production production-worthy states.

The yield management system (YMS) 120 analyzes end-of-line data such aselectronic test (e-test) data to determine product yield. Theend-of-line data may include wafer acceptance testing (WAT), wafer sortresults and/or final test operations. The yield manager 120 can provideproduct yield trends, lot level analysis of product yield, yieldcorrelation to manufacturing processes, statistical analysis of yield,etc. In one embodiment, the YMS 120 uses integrated circuit design,visible defect, parametric and e-test data to identify causes of lowyield.

In one example, with many maintenance events in semiconductormanufacturing, process “tuning” is required as part of the maintenancerecovery process, where test wafers are processed and measured and theprocess is adjusted based on the results. The process is determinedready-for-production when the test wafer measurements meet specifiedquality criteria. This tuning can oftentimes be costly both in terms ofwafers and lost production time. The tuning process itself can often beinexact with adjustments determined manually and often in a univariate(one-by-one or a-few-by-one) fashion.

In one embodiment, the tuning process can be improved by utilizingmultivariate R2R control along with VM (as necessary) to more preciselydetermine tuning recommendations and reduce tuning iteration steps. Amultivariate analysis is based on a statistical principle ofmultivariate statistics in which observation and analysis of more thanone statistical outcome variable occurs at a time.

FIG. 3 illustrates a flow diagram of one embodiment for a computerimplemented method of multivariate analysis utilizing run-to-run controland virtual metrology to reduce MTTR and improve G2G time during postpreventative maintenance (PM) recovery. The method may be performed byprocessing logic that may comprise hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), or a combination thereof. Inone embodiment, a computer implemented method 300 is performed by theequipment engineering system 105 or some other system (e.g., a systemhosting a VM multi-algorithm prediction subsystem 107, R2R controller,and coupled to the EES 105 via a network). The computer implementedmethod 300 is designed to transition a manufacturing tool to an ideal ornearly ideal operating region after PM (or any maintenance) with someconstraints. The ideal or nearly ideal operating region is defined bykey parameters (e.g., thickness profile, electrical properties, etc.).Constraints may include tuning parameters (e.g., gas flows, temperature)that have certain boundaries and possible relationships with othertuning parameters or variables in order for the tool to be qualified fora production state. The tool is transitioned to the production statetypically using multiple iterations of test substrates and adjustingtuning parameters.

Referring to FIG. 3, the computer implemented method 300 includesprocessing test substrates with nominal values of tuning parameters (orvalues defined by an operator of a manufacturing tool such as a processengineer or technician) of at least one manufacturing tool at operation301. The computer implemented method 300 includes collecting data (e.g.,test substrate data obtained from measurements of the test substrates,other metrology data, fault detection data, thickness statisticalprocess control (SPC) data, etc.) by a system (e.g., an equipmentengineering system) at operation 302. The collected data includes dataassociated with a manufacturing process, the at least one manufacturingtool and/or a manufactured product. Processing logic of the systemdetermines a relationship between tool parameter settings (e.g.,temperature, lamp power ratios, gas flows during process recipes,chamber pressure, downforce for a chemical mechanical planarizationtool, etc.) for the at least one manufacturing tool and the collecteddata (e.g., test substrate data, other metrology data, fault detectiondata, thickness statistical process control (SPC) data, etc.) atoperation 304. Processing logic then compares test substrate data to atleast one target parameter (e.g., a range of target values for eachtarget parameter) for the test substrates of the at least onemanufacturing tool at operation 305. If at least one target parameter(e.g., electrical parameters) or metrology data is not measured oravailable, then processing logic of the system utilizes zero or more (orat least one) virtual metrology predictive algorithms (e.g., PartialLeast Squared (PLS), Support Vector Regression (SVR), etc.) and at leastsome of the collected data to obtain a metrology prediction for the atleast one target parameter or metrology data that is not measured oravailable at operation 306.

Processing logic of the system determines whether the test substratedata satisfies the at least one target parameter (e.g., within a rangeof target values for each target parameter) at operation 308. If so,then the processing logic completes the requalification process atoperation 320.

Otherwise, processing logic then applies R2R control modeling (e.g.,linear, nonlinear) to obtain a state estimation including a currentoperating region and condition of the at least one manufacturing toolbased on the test substrate data (e.g., measurements obtained from thetest substrates at operation 301) and the corresponding tuningparameters applied during the processing of the test substrates atoperation 310. The processing logic of the system provides recommendedtool parameter adjustments of a tool parameter adjustment event to moveor transition the current operating region of the at least manufacturingtool to an ideal or nearly ideal operating region having ideal or nearlyideal target parameters based on the R2R control modeling at operation312. A virtual metrology predictive algorithm if virtual metrology isnecessary is tuned prior to or during its use in a tool parameteradjustment event of the tool parameter adjustments The method proceedsto operation 301 for a next iteration with the recommended (or similar)tool parameter adjustments having at least one different tuningparameter than the initial iteration at operation 301. In this manner,the method 300 reduces a tool or component downtime after PM orunplanned maintenance during maintenance recovery and requalificationwhich results in higher product output. Thus, better utilization ofmanufacturing tools increases profits for the manufacturing environment.

In another example, the method 300 does not include utilizing one ormore virtual metrology predictive algorithms to obtain a metrologyprediction for the at least one target parameter that is not measured oravailable at operation 306. FIGS. 4A-4C illustrate one implementation ofthe method 300 for a deposition tool in accordance with one embodiment.FIG. 4A illustrates a plot 400 of thickness versus gas flow fordifferent temperatures of a deposition tool in accordance with oneembodiment. The different temperatures includes T1, T2, and T3. R2Rcontrol modeling uses this plot 400 to determine a current operatingregion and condition of the deposition tool based on the test substratedata (e.g., thickness measurements obtained from the test substrates,operation 301) illustrated in FIG. 4A and the corresponding tuningparameters (e.g., temperature, gas flow) applied during the processingof the test substrates. If test substrate data is not available, then VMcan be used for predicting metrology data. The R2R control modelingmodels this plot 400 with the following equation:

$\begin{bmatrix}y_{CentralThickness} \\y_{{Ge}\mspace{11mu} \%}\end{bmatrix} = \begin{bmatrix}{f_{1}\left( {x_{{GeH}\; 4},x_{DCS},x_{HCI},x_{B\; 2H\; 6}} \right)} \\{f_{2}\left( {x_{{GeH}\; 4},x_{DCS},x_{HCI},x_{B\; 2\; H\; 6}} \right)}\end{bmatrix}$

FIG. 4B illustrates a plot 420 of thickness versus gas flow for atemperature T* of a deposition tool in accordance with one embodiment.R2R control modeling obtains a state estimation including a currentoperating region and condition of the deposition tool as illustrated inplot 420. The R2R control model estimate state for the deposition toolwith the following equation:

$\begin{bmatrix}y_{CentralThickness} \\y_{{Ge}\mspace{11mu} \%}\end{bmatrix} = \begin{bmatrix}{f_{1}^{*}\left( {x_{{GeH}\; 4},x_{DCS},x_{HCI},x_{B\; 2H\; 6}} \right)} \\{f_{2}\left( {x_{{GeH}\; 4},x_{DCS},x_{HCI},x_{B\; 2\; H\; 6}} \right)}\end{bmatrix}$

FIG. 4C illustrates a plot 440 of thickness versus gas flow for atemperature T* of a deposition tool for modeling predictive control inaccordance with one embodiment. This plot 440 includes a target 450having a target central thickness 452 and a target gas flow 454 (e.g.,target Ge dopant % for set of gas flows). R2R control modeling providesrecommended tool parameter adjustments to move or transition the currentoperating region of the deposition tool to an ideal or nearly idealoperating region having ideal or nearly ideal target parameters based onthe R2R control modeling. The R2R control modeling models the ideal ornearly ideal operating region for the deposition tool with the followingequation:

$\begin{bmatrix}x_{{GeH}\; 4} \\x_{DCS} \\x_{HCI} \\x_{B\; 2H\; 6}\end{bmatrix} = {\min\limits_{X_{{GeH}\; 4},X_{DCS},X_{HCI},X_{B\; 2H\; 6}}\begin{Bmatrix}{{y_{CentralThickness}^{Target} - {f_{1}^{*}\left( {x_{{GeH}\; 4},x_{DCS},x_{HCI},x_{B\; 2H\; 6}} \right)}}} \\{{y_{{Ge}\mspace{14mu} \%}^{Target} - {f_{2}^{*}\left( {x_{{GeH}\; 4},x_{DCS},x_{HCI},x_{B\; 2H\; 6}} \right)}}}\end{Bmatrix}}$

For one example in semiconductor manufacturing, a R2R controlmaintenance recovery approach can be applied to a thermal process, wherethe lamp maintenance effort can be costly and time consuming.

FIG. 5A illustrates lamp failure modes in accordance with oneembodiment. In a typical system, lamps can fail unexpectedly causingunscheduled downtime and scrap. The lamp failure modes include afilament 410 that is sagging below a center line, a short circuit 412between filament helix and support pillar, and a short circuit 414between turns. The maintenance recovery can be time consuming as thereare usually multiple post-maintenance (i.e., after lamp kit replacement)iterations of lamp parameter “tuning” that include running a number oftest wafers with specific characterization recipes, analyzing metrologydata, and making hardware and software adjustments. This processcontinues until the metrology data meets specified quality criteria.Four to ten iterations of this type are not uncommon leading to MTTR onthe order of 2 days or more.

FIG. 5B illustrates a diagram in which multivariate R2R control with VMis applied in accordance with one embodiment. PM process metrology 502is utilized along with VM models based on FD output data 504 todetermine a state of the system. PM tuning models utilize this stateinformation to determine tuning advices in a multivariate fashion. Theresult is that fewer tuning iterations are required to bring the chamberto a satisfactory matched state for release back into production.

FIG. 6 illustrates a diagram in which multivariate R2R and VM models areapplied during maintenance recovery in accordance with one embodiment.In one example, more than one tuning iteration is usually requiredbecause the R2R control tuning model often has to be re-centered withthe first set of metrology results. This is due to the variability inand length of time between PMs. Note also that VM information used toenhance the determination of a system state has been shown to provide animproved R2R control system capability. However, depending on themaintenance event type and tuning procedures, it may not always benecessary (i.e., PM process metrology may be sufficient). The diagram600 illustrates test wafer quality (normalized) on a vertical axisversus tuning iterations for chambers A and B on a horizontal axis. Forchamber A with no R2R control and VM, 5 iterations were need formaintenance recovery. For chamber A with R2R control and VM, only 2iterations were need for maintenance recovery. Thus, the R2R control andVM during the maintenance recovery reduces the MTTR by 3 iterations.

For chamber B with no R2R control and VM, 3 iterations were need formaintenance recovery. For chamber B with R2R control and VM, only 2iterations were need for maintenance recovery. Thus, the R2R control andVM during the maintenance recovery reduces the MTTR by 1 iteration.

In one example of a thin film deposition PM (e.g., CVD PM, epitaxial PM,etc.), a recovery period typically takes at least five tuning iterationsfor testing processing recipes with test substrates, performingmetrology (e.g., measuring thickness profiles, determining dopantconcentrations, etc.) for multiple recipes, and then making tuningadjustments for returning a film deposition tool to a production state.This causes PM recovery time period of greater than 3 days in which thedeposition tool cannot be used in the production state for producingproduct.

FIG. 7 illustrates a diagram in which multivariate R2R control with VMis applied in accordance with one embodiment. Process metrology data 702(e.g., SPC data, test substrate data, FD data, film thickness SPC data,etc.) and tool parameter and sensor data for at least one manufacturingtool are received as inputs for a multivariate prediction model 720 todetermine state information of at least one system, equipment, ormanufacturing tool. Process tuning models of the MVA prediction modelutilize this state information to determine tuning advices in amultivariate fashion. The result is that fewer tuning iterations arerequired to bring the system or tool to a satisfactory state for releaseback into a production state.

In one example of MTTR modeling, a system collects historical testsubstrate data (e.g., FD data, film thickness SPC data, etc.). Thesystem then determines a relationship between tool parameters settingsand SPC data that corresponds to the tool parameter settings.Multivariate models are then utilized to rapidly identify criticalparameters of the manufacturing tool to be adjusted or tuned. The modelscan identify values for multivariate variables or parameters that areout of tool or process specifications and then make appropriatecorrections. In this manner, a downtime (i.e., non-production state) ofthe manufacturing tool is significantly reduced resulting in additionalproduct output. For example, a number of tuning iterations can bereduced from at least 5 to 2 or 3 tuning iterations.

FIG. 8 illustrates a diagram in which multivariate R2R control with VMis applied in accordance with one embodiment for reducing maintenancerecovery time. Process metrology data 802 (e.g., SPC data, testsubstrate data, FD data, film thickness SPC data, etc.) and toolparameter and sensor data 804 for at least one manufacturing tool arereceived as inputs for at least one of the VM module 810 and therun-to-run control module 820. The VM module 810 utilizes at least oneprediction algorithm for a virtual metrology function which can be alinear or nonlinear function F (u_(k)) with u_(k) being sensor data(e.g., temperature, lamp power, lamp power ratios, gas flows forprocessing gases during a processing recipe, etc.) for at least onemanufacturing tool at time k. The linear or nonlinear function F (u_(k))generates a metrology prediction 812 based on at least the sensor data804. The metrology prediction 812 may also be based on metrology data802. The R2R controller 820 receives the metrology prediction 812 anddetermines tool parameter adjustments 830 based on the metrologyprediction and R2R parameters including sensor data, a state x_(k) attime k (e.g., a state of the manufacturing tool at time k such as), astate x_(k+1) at time k+1 (e.g., a state of the manufacturing tool attime k+1), sensor noise w_(k), metrology measurement noise v_(k),metrology measurement y_(k) at time k, a state transition function f(*),and observation function g(*).

In one embodiment, the R2R controller 820 utilizes the followingequations for generating tool parameter adjustments 830:

x _(k+1) =f(x _(k) ,u _(k) ,w _(k))

y _(k) =g(x _(k))+v _(k)

Improved knowledge of a state x_(k+1) at time k+1 (e.g., a state of themanufacturing tool at time k+1) and identification of criticalparameters leads to a reduced number of tuning iterations for toolparameters adjustments 830. The manufacturing tool can be returned to aproduction state in a shorter time period with reduced maintenancerecovery and requalification.

In another embodiment, the R2R controller 820 does not need themetrology prediction 812 for determining tool parameter adjustments 830.

In this manner, the R2R controller 820 utilizes this state informationto determine tuning advices in a multivariate fashion.

In one example, the VM module 810 and R2R controller 820 are utilized tomake adjustments to tool parameters based on a first set of inputparameters (e.g., 3-5 input parameters). After the adjustments are madeto tool parameters (e.g., process recipes) then the VM module 810 andR2R controller 820 are again utilized to make adjustments to toolparameters based on a different second set of input parameters (e.g.,3-5 input parameters).

FIG. 9 illustrates an exemplary architecture of a system (e.g.,equipment engineering system (EES)), in accordance with one embodiment.In one embodiment, the system 900 is implemented with an Applied E3™ APCInfrastructure in which methods of the present disclosure areintegrated. The EES 900 leverages an E3 application adapter 610 thatprovides an interface to Web services. Multivariate prediction module920 can be integrated through Web services. This multivariate predictionmodule 920 integration approach enables rapid prototyping,customization, and technology transfer. The multivariate predictionmodule 920 includes a predictive VM module 922 that enables the EES 900to utilize and determine predictive algorithms for adaptive virtualmetrology and also R2R control module 924 for reducing tuning iterationsfor post maintenance recovery.

The adapter 910 communicates with the strategy engine 930, the clienthandler 940, the data service provider 950, and the log server 960. Thestrategy engine 930 includes general blocks 931, run to run blocks 932(e.g., R2R controller, R2R module), FD blocks 933, EPT blocks 934, andcustom blocks 935. The FD blocks 933 obtain FD data. The run to runblocks 932 include pre-configured or adaptive R2R models for implementedoperations of methods and embodiments of the present disclosure. The EPTblocks 934 obtain equipment performance tracking information. The dataaccess layer 970 provides access to a database 980. This database 980includes process data, FDC/EPT/R2R data, control rules, and datacollection plans. The discovery manager 990 provides discovery featuresfor identifying capabilities integrated into the system. The strategyengine is used to govern the interaction of blocks in terms of“strategies” to achieve specific objectives in response to eventsreceived.

For example, for substrate-to-substrate control (e.g., Wafer-to-Wafer(W2W) Control), a strategy housed by the strategy engine 930 could beenvisioned that captures FD outputs from a FD implementation formulatedwith the FD blocks 933 and stored in the database 980, sends thisinformation to a formulation in the multivariate prediction module 920(integrated via the web-services adaptor 910) for calculation of VMoutputs (e.g., metrology predictions), determination of tool parameteradjustments using R2R 924 (or alternatively R2R 932), and output thistool parameter adjustments for reducing tuning iterations after PM andduring requalification state. Collected metrology data is used to updateVM models.

There are a number of extensions to prediction algorithms that utilizefeedback of actual output measurement data, such as metrology or yieldanalysis, to continually improve or “tune” the prediction models. As anexample, NIPALS and EWMA (Exponentially Weighted Moving Average) are twodocumented adaptive extensions to the Project on Latent Structureprediction mechanisms. In one embodiment of this invention, the VMalgorithms are tuned as necessary at the start of an MTTR event toaccount for changes in process, equipment or other conditions betweendowntimes that require an adjustment of the VM model. The type and levelof adjustment can be determined by techniques such as a VM switchingalgorithm. These various extensions for handling the dynamics performdifferently depending on the prediction and adaptation environment.Further many of the extensions also represent a tradeoff betweencomputational complexity or time, and accuracy.

VM models include a predictive algorithm having a VM predictionequation:

S=B*t+c

In some embodiments, S is a predicted output, B represents a matrix, tis an input factor, and c is zero'th order term. S, B, t, and c arecomponents vectors or matrices. Given two prediction adaptationalgorithms EWMA and NIPALS, EWMA is fast and easy, but can be inaccuratewhen the VM equation changes. The EWMA can utilize zero'th orderadaptation of the VM equation (e.g., updates the “c” vector). NIPALS iscomplex, but more accurate. NIPALS reformulates the VM equation (e.g.,updates both “B” and “c”). The VM multi-algorithm prediction subsystem107 may compare predictions of metrology data (Y′) to actual metrologydata (Y) on occasion with this difference being E.

FIG. 10 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 1000 in accordance with oneembodiment within which a set of instructions, for causing the machineto perform any one or more of the methodologies discussed herein, may beexecuted. In alternative embodiments, the machine may be connected(e.g., networked) to other machines in a Local Area Network (LAN), anintranet, an extranet, or the Internet. The machine may operate in thecapacity of a server or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a server, a network router, switchor bridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines(e.g., computers) that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The exemplary computer system 1000 includes a processor 1002, a mainmemory 1004 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), etc.), a static memory 1006 (e.g., flash memory, static randomaccess memory (SRAM), etc.), and a secondary memory 1018 (e.g., a datastorage device), which communicate with each other via a bus 1030.

Processor 1002 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 1002 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1002 may alsobe one or more special-purpose processing devices such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), network processor, or thelike. Processor 1002 is configured to execute the processing logic 1026for performing the operations and steps discussed herein.

The computer system 1000 may further include a network interface device1008. The computer system 1000 also may include a video display unit1010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)),an alphanumeric input device 1012 (e.g., a keyboard), a cursor controldevice 1014 (e.g., a mouse), and a signal generation device 1016 (e.g.,a speaker).

The secondary memory 1018 may include a machine-readable storage medium(or more specifically a computer-readable storage medium) 1031 on whichis stored one or more sets of instructions (e.g., software 1022)embodying any one or more of the methodologies or functions describedherein. The software 1022 may also reside, completely or at leastpartially, within the main memory 1004 and/or within the processingdevice 1002 during execution thereof by the computer system 1000, themain memory 1004 and the processing device 1002 also constitutingmachine-readable storage media. The software 1022 may further betransmitted or received over a network 1020 via the network interfacedevice 1008.

The machine-readable storage medium 1031 may also be used to store oneor more subsystems of a yield management system (YMS) 1020, an equipmentengineering system (EES) 105 and/or a manufacturing execution system(MES) 110 (as described with reference to FIG. 1), and/or a softwarelibrary containing methods that call subsystems of a YMS, EES and/orMES. The machine-readable storage medium 1031 may further be used tostore one or more additional components of a manufacturing informationand control system (MICS), such as a decision support logic component, areal-time monitor, and/or an execution logic component. While themachine-readable storage medium 1031 is shown in an exemplary embodimentto be a single medium, the term “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present invention. The term“machine-readable storage medium” shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia.

In one embodiment, a computer system includes a memory to store one ormore sets of instructions and a processor that is coupled to the memory.The processor is configured to execute instructions to collect dataincluding test substrate data and fault detection data for maintenancerecovery of at least one manufacturing tool in a manufacturing facility,determine a relationship between tool parameter settings for the atleast one manufacturing tool and at least some collected data includingthe test substrate data. The method further includes utilizing zero ormore virtual metrology predictive algorithms and at least some collecteddata to obtain a metrology prediction and applying multivariaterun-to-run (R2R) control modeling to obtain a state estimation includinga current operating region of the at least one manufacturing tool basedon the test substrate data and obtain at least one tool parameteradjustment for at least one target parameter for the at least onemanufacturing tool. In one example, the R2R control modeling utilizesthe following parameters: sensor data obtained from a sensor of the atleast manufacturing tool, state at time k, state at time k+1, sensornoise, metrology measurement noise, metrology measurement at time k, astate transition matrix, a process sensitivity matrix, and anobservation model matrix.

In one example, the virtual metrology predictive algorithm is tunedprior to or during its use in a tool parameter adjustment event of theat least one tool parameter adjustment. In one embodiment, the collecteddata includes a thickness profile and a dopant concentration formaintenance recovery of a deposition tool. The tool parameteradjustments include adjusting a temperature parameter, lamp powerratios, and gas flow parameters for the deposition tool.

In one example, applying multivariate run-to-run (R2R) control modelingto obtain tool parameter adjustments for the at least one manufacturingtool occurs after maintenance to reduce maintenance recovery time and toreduce requalification time.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. Although the present invention has been describedwith reference to specific exemplary embodiments, it will be recognizedthat the invention is not limited to the embodiments described, but canbe practiced with modification and alteration within the spirit andscope of the appended claims. Accordingly, the specification anddrawings are to be regarded in an illustrative sense rather than arestrictive sense. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A computer implemented method comprising:collecting, with a system, data including test substrate data or othermetrology data and fault detection data for maintenance recovery of atleast one manufacturing tool in a manufacturing facility; determining,with the system, a relationship between tool parameter settings for theat least one manufacturing tool and at least some collected dataincluding the test substrate data; utilizing zero or more virtualmetrology predictive algorithms and at least some collected data toobtain a metrology prediction; and applying multivariate run-to-run(R2R) control modeling to obtain a state estimation including a currentoperating region of the at least one manufacturing tool based on thetest substrate data and obtain at least one tool parameter adjustmentfor at least one target parameter for the at least one manufacturingtool, wherein applying multivariate run-to-run (R2R) control modeling toobtain tool parameter adjustments for at least one manufacturing tooloccurs after maintenance to reduce maintenance recovery time and toreduce requalification time.
 2. The computer implemented method of claim1, wherein the R2R control modeling utilizes the following parameters:sensor data obtained from a sensor of the at least manufacturing tool,state at time k, state at time k+1, sensor noise, metrology measurementnoise, metrology measurement at time k, a state transition matrix, aprocess sensitivity matrix, and an observation model matrix.
 3. Thecomputer implemented in method of claim 1, wherein the virtual metrologypredictive algorithm is tuned prior to or during its use in a toolparameter adjustment event of the at least one tool parameteradjustment.
 4. The computer implemented method of claim 1, wherein thecollected data includes a thickness profile and a dopant concentrationfor maintenance recovery of a deposition tool.
 5. The computerimplemented method of claim 4, wherein the tool parameter adjustmentsincludes adjusting a temperature parameter, lamp power ratios, and gasflow parameters for the deposition tool.
 6. The computer implementedmethod of claim 1, further comprising: determining whether the testsubstrate data satisfies the at least one target parameter.
 7. Acomputer-readable storage medium comprising executable instructions tocause a processor to perform operations, the instructions comprising:collecting, with a system, data including test substrate data ormetrology data and fault detection data for maintenance recovery of atleast one manufacturing tool in a manufacturing facility; determining,with the system, a relationship between tool parameter settings for theat least one manufacturing tool and at least some collected dataincluding the test substrate data; utilizing zero or more virtualmetrology predictive algorithms and at least some collected data toobtain a metrology prediction; and applying multivariate run-to-run(R2R) control modeling to obtain a state estimation including a currentoperating region of the at least one manufacturing tool based on thetest substrate data and obtain at least one tool parameter adjustmentfor at least one target parameter for the at least one manufacturingtool, wherein applying multivariate run-to-run (R2R) control modeling toobtain tool parameter adjustments for at least one manufacturing tooloccurs after maintenance to reduce maintenance recovery time and toreduce requalification time
 8. The computer-readable storage medium ofclaim 7, wherein the R2R control modeling utilizes the followingparameters: sensor data obtained from a sensor of the at leastmanufacturing tool, state at time k, state at time k+1, sensor noise,metrology measurement noise, metrology measurement at time k, a statetransition matrix, a process sensitivity matrix, and an observationmodel matrix.
 9. The computer-readable storage medium of claim 8,wherein the virtual metrology predictive algorithm is tuned prior to orduring its use in a tool parameter adjustment event of the at least onetool parameter adjustment.
 10. The computer-readable storage medium ofclaim 7, wherein the collected data includes a thickness profile and adopant concentration for maintenance recovery of a deposition tool. 11.The computer-readable storage medium of claim 10, wherein the toolparameter adjustments includes adjusting a temperature parameter, lamppower ratios, and gas flow parameters for the deposition tool.
 12. Thecomputer implemented method of claim 7, further comprising: determiningwhether the test substrate data satisfies the at least one targetparameter.
 13. A computer system comprising: a memory to store one ormore sets of instructions; and a processor, coupled to the memory, isconfigured to execute instructions to: determining, with the system, arelationship between tool parameter settings for the at least onemanufacturing tool and at least some collected data including the testsubstrate data; utilizing zero or more virtual metrology predictivealgorithms and at least some collected data to obtain a metrologyprediction; applying multivariate run-to-run (R2R) control modeling toobtain a state estimation including a current operating region of the atleast one manufacturing tool based on the test substrate data and obtainat least one tool parameter adjustment for at least one target parameterfor the at least one manufacturing tool, wherein applying multivariaterun-to-run (R2R) control modeling to obtain tool parameter adjustmentsfor at least one manufacturing tool occurs after maintenance to reducemaintenance recovery time and to reduce requalification time.
 14. Thecomputer system of claim 13, wherein the R2R control modeling utilizesthe following parameters: sensor data obtained from a sensor of the atleast manufacturing tool, state at time k, state at time k+1, sensornoise, metrology measurement noise, metrology measurement at time k, astate transition matrix, a process sensitivity matrix, and anobservation model matrix.
 15. The computer system of claim 14, whereinthe virtual metrology predictive algorithm is tuned prior to or duringits use in a tool parameter adjustment event of the at least one toolparameter adjustment.
 16. The computer system of claim 13, wherein thecollected data includes a thickness profile and a dopant concentrationfor maintenance recovery of a deposition tool.
 17. The computer systemof claim 16, wherein the tool parameter adjustments includes adjusting atemperature parameter, lamp power ratios, and gas flow parameters forthe deposition tool.
 18. The computer system of claim 13, furthercomprising: determining whether the test substrate data satisfies the atleast one target parameter.